Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories.
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ABSTRACT:
SUBMITTER: Lali R
PROVIDER: S-EPMC8494733 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Massively parallel sequencing technologies provide great opportunities for discovering rare susceptibility variants involved in complex disease etiology via large-scale imputation and exome and whole-genome sequence-based association studies. Due to modest effect sizes, large sample sizes of tens to hundreds of thousands of individuals are required for adequately powered studies. Current analytical tools are obsolete when it comes to handling these large datasets. To facilitate the analysis of large-scale sequence-based studies, we developed SEQSpark which implements parallel processing based on Spark to increase the speed and efficiency of performing data quality control, annotation, and association analysis. To demonstrate the versatility and speed of SEQSpark, we analyzed whole-genome sequence data from the UK10K, testing for associations with waist-to-hip ratios. The analysis, which was completed in 1.5 hr, included loading data, annotation, principal component analysis, and single variant and rare variant aggregate association analysis of >9 million variants. For rare variant aggregate analysis, an exome-wide significant association (p < 2.5 × 10-6) was observed with CCDC62 (SKAT-O [p = 6.89 × 10-7], combined multivariate collapsing [p = 1.48 × 10-6], and burden of rare variants [p = 1.48 × 10-6]). SEQSpark was also used to analyze 50,000 simulated exomes and it required 1.75 hr for the analysis of a quantitative trait using several rare variant aggregate association methods. Additionally, the performance of SEQSpark was compared to Variant Association Tools and PLINK/SEQ. SEQSpark was always faster and in some situations computation was reduced to a hundredth of the time. SEQSpark will empower large sequence-based epidemiological studies to quickly elucidate genetic variation involved in the etiology of complex traits.
| S-EPMC5501866 | biostudies-literature
Project description:We carried out analyses with the goal of identifying rare variants in exome sequence data that contribute to disease risk for a complex trait. We analyzed a large, 47-member, multigenerational pedigree with 11 cases of autism spectrum disorder, using genotypes from 3 technologies representing increasing resolution: a multiallelic linkage marker panel, a dense diallelic marker panel, and variants from exome sequencing. Genome-scan marker genotypes were available on most subjects, and exome sequence data was available on 5 subjects. We used genome-scan linkage analysis to identify and prioritize the chromosome 22 region of interest, and to select subjects for exome sequencing. Inheritance vectors (IVs) generated by Markov chain Monte Carlo analysis of multilocus marker data were the foundation of most analyses. Genotype imputation used IVs to determine which sequence variants reside on the haplotype that co-segregates with the autism diagnosis. Together with a rare-allele frequency filter, we identified only one rare variant on the risk haplotype, illustrating the potential of this approach to prioritize variants. The associated gene, MYH9, is biologically unlikely, and we speculate that for this complex trait, the key variants may lie outside the exome.
| S-EPMC3722055 | biostudies-literature
Project description:Many population-based rare-variant (RV) association tests, which aggregate variants across a region, have been developed to analyze sequence data. A drawback of analyzing population-based data is that it is difficult to adequately control for population substructure and admixture, and spurious associations can occur. For RVs, this problem can be substantial, because the spectrum of rare variation can differ greatly between populations. A solution is to analyze parent-child trio data, by using the transmission disequilibrium test (TDT), which is robust to population substructure and admixture. We extended the TDT to test for RV associations using four commonly used methods. We demonstrate that for all RV-TDT methods, using proper analysis strategies, type I error is well-controlled even when there are high levels of population substructure or admixture. For trio data, unlike for population-based data, RV allele-counting association methods will lead to inflated type I errors. However type I errors can be properly controlled by obtaining p values empirically through haplotype permutation. The power of the RV-TDT methods was evaluated and compared to the analysis of case-control data with a number of genetic and disease models. The RV-TDT was also used to analyze exome data from 199 Simons Simplex Collection autism trios and an association was observed with variants in ABCA7. Given the problem of adequately controlling for population substructure and admixture in RV association studies and the growing number of sequence-based trio studies, the RV-TDT is extremely beneficial to elucidate the involvement of RVs in the etiology of complex traits.
| S-EPMC3882934 | biostudies-literature
Project description:BackgroundStudies have identified many common genetic associations that influence renal function and all-cause CKD, but these explain only a small fraction of variance in these traits. The contribution of rare variants has not been systematically examined.MethodsWe performed exome sequencing of 3150 individuals, who collectively encompassed diverse CKD subtypes, and 9563 controls. To detect causal genes and evaluate the contribution of rare variants we used collapsing analysis, in which we compared the proportion of cases and controls carrying rare variants per gene.ResultsThe analyses captured five established monogenic causes of CKD: variants in PKD1, PKD2, and COL4A5 achieved study-wide significance, and we observed suggestive case enrichment for COL4A4 and COL4A3. Beyond known disease-associated genes, collapsing analyses incorporating regional variant intolerance identified suggestive dominant signals in CPT2 and several other candidate genes. Biallelic mutations in CPT2 cause carnitine palmitoyltransferase II deficiency, sometimes associated with rhabdomyolysis and acute renal injury. Genetic modifier analysis among cases with APOL1 risk genotypes identified a suggestive signal in AHDC1, implicated in Xia-Gibbs syndrome, which involves intellectual disability and other features. On the basis of the observed distribution of rare variants, we estimate that a two- to three-fold larger cohort would provide 80% power to implicate new genes for all-cause CKD.ConclusionsThis study demonstrates that rare-variant collapsing analyses can validate known genes and identify candidate genes and modifiers for kidney disease. In so doing, these findings provide a motivation for larger-scale investigation of rare-variant risk contributions across major clinical CKD categories.
| S-EPMC6551770 | biostudies-literature
Project description:In biobank data analysis, most binary phenotypes have unbalanced case-control ratios, and this can cause inflation of type I error rates. Recently, a saddle point approximation (SPA) based single-variant test has been developed to provide an accurate and scalable method to test for associations of such phenotypes. For gene- or region-based multiple-variant tests, a few methods exist that can adjust for unbalanced case-control ratios; however, these methods are either less accurate when case-control ratios are extremely unbalanced or not scalable for large data analyses. To address these problems, we propose SKAT- and SKAT-O- type region-based tests; in these tests, the single-variant score statistic is calibrated based on SPA and efficient resampling (ER). Through simulation studies, we show that the proposed method provides well-calibrated p values. In contrast, when the case-control ratio is 1:99, the unadjusted approach has greatly inflated type I error rates (90 times that of exome-wide sequencing α = 2.5 × 10-6). Additionally, the proposed method has similar computation time to the unadjusted approaches and is scalable for large sample data. In our application, the UK Biobank whole-exome sequence data analysis of 45,596 unrelated European samples and 791 PheCode phenotypes identified 10 rare-variant associations with p value < 10-7, including the associations between JAK2 and myeloproliferative disease, HOXB13 and cancer of prostate, and F11 and congenital coagulation defects. All analysis summary results are publicly available through a web-based visual server, and this availability can help facilitate the identification of the genetic basis of complex diseases.
| S-EPMC7042481 | biostudies-literature
Project description:Schizophrenia is a highly heritable disorder. Genome-wide association studies based largely on common alleles have identified over 100 schizophrenia risk loci, but it is also evident from studies of copy number variants (CNVs) and from exome-sequencing studies that rare alleles are also involved. Full characterization of the contribution of rare alleles to the disorder awaits the deployment of sequencing technology in very large sample sizes, meanwhile, as an interim measure, exome arrays allow rare non-synonymous variants to be sampled at a fraction of the cost. In an analysis of exome array data from 13 688 individuals (5585 cases and 8103 controls) from the UK, we found that rare (minor allele frequency < 0.1%) variant association signal was enriched among genes that map to autosomal loci that are genome-wide significant (GWS) in common variant studies of schizophrenia genome-wide association study (PGWAS = 0.01) as well as gene sets known to be enriched for rare variants in sequencing studies (PRARE = 0.026). We also identified the gene-wise equivalent of GWS support for WDR88 (WD repeat-containing protein 88), a gene of unknown function (P = 6.5 × 10(-7)). Rare alleles represented on exome chip arrays contribute to the genetic architecture of schizophrenia, but as is the case for GWAS, very large studies are required to reveal additional susceptibility alleles for the disorder.
| S-EPMC4754044 | biostudies-literature
Project description:IntroductionEssential tremor (ET) is one of the most common movement disorders. Despite its high prevalence and heritability, its genetic etiology remains elusive with only a few susceptibility genes identified and poorly replicated. Our aim was to find novel candidate genes involved in ET predisposition through whole exome sequencing.MethodsWe studied eight multigenerational families (N = 40 individuals) with an autosomal-dominant inheritance using a comprehensive strategy combining whole exome sequencing followed by case-control association testing of prioritized variants in a separate cohort comprising 521 ET cases and 596 controls. We further performed gene-based burden analyses in an additional dataset comprising 789 ET patients and 770 healthy individuals to investigate whether there was an enrichment of rare deleterious variants within our candidate genes.ResultsFifteen variants co-segregated with disease status in at least one of the families, among which rs749875462 in CCDC183, rs535864157 in MMP10 and rs114285050 in GPR151 showed a nominal association with ET. However, we found no significant enrichment of rare variants within these genes in cases compared with controls. Interestingly, MMP10 protein is involved in the inflammatory response to neuronal damage and has been previously associated with other neurological disorders.ConclusionsWe prioritized a set of promising genes, especially MMP10, for further genetic and functional studies in ET. Our study suggests that rare deleterious coding variants that markedly increase susceptibility to ET are likely to be found in many genes. Future studies are needed to replicate and further infer biological mechanisms and potential disease causality for our identified genes.
| S-EPMC7856267 | biostudies-literature
Project description:Background and aims: As genome sequencing technologies rapidly expand in capacity and availability, understanding how genetic background in different populations modifies IBD risk will be an important factor in disease prediction, prevention, and treatment. However, most of the datasets that are used to generate polygenic risk scores (PRS) contain predominantly European ancestry patients. To address this, we tested different models for prediction of IBD cases using PRS built using association data from multiple races and also assessed the penetrance of rare very early onset IBD (VEOIBD) SNPs. Methods: PRS were calculated using association data from European, African American, and Ashkenazi Jewish (AJ) studies, as well as a meta-GWAS run using all three association datasets. PRS were then combined using regression modelling to assess which combination of scores was best able to predict IBD status in European, AJ, Hispanic, and African American BioMe Biobank populations. Additionally, rare variants were assessed in genes associated with very early onset IBD, taking into account genetic penetrance in each BioMe population, deleteriousness, and evolutionary conservation. Results: Combining risk scores based on IBD association results from multiple racial populations resulted in improved IBD prediction for every population in BioMe. We also identified highly penetrant rare variants in previously established VEOIBD genes which were predicted to be deleterious, including SNPs in established risk genes such as NOD2 as well as novel variants, including some in LRBA which appear to be particularly relevant for risk of IBD in African Americans.
2021-04-07 | GSE150516 | GEO
Project description:OBJECTIVE:To determine whether a monogenic basis explains sudden infant death syndrome (SIDS) using an exome-wide focus. STUDY DESIGN:A cohort of 427 unrelated cases of SIDS (257 male; average age?=?2.7?±?1.9 months) underwent whole-exome sequencing. Exome-wide rare variant analyses were carried out with 278 SIDS cases of European ancestry (173 male; average age?=?2.7?±?1.98 months) and 973 ethnic-matched controls based on 6 genetic models. Ingenuity Pathway Analysis also was performed. The cohort was collected in collaboration with coroners, medical examiners, and pathologists by St George's University of London, United Kingdom, and Mayo Clinic, Rochester, Minnesota. Whole-exome sequencing was performed at the Genomic Laboratory, Kings College London, United Kingdom, or Mayo Clinic's Medical Genome Facility, Rochester, Minnesota. RESULTS:Although no exome-wide significant (P?<?2.5?×?10-6) difference in burden of ultra-rare variants was detected for any gene, 405 genes had a greater prevalence (P?<?.05) of ultra-rare nonsynonymous variants among cases with 17 genes at P?<?.005. Some of these potentially overrepresented genes may represent biologically plausible novel candidate genes for a monogenic basis for a portion of patients with SIDS. The top canonical pathway identified was glucocorticoid biosynthesis (P?=?.01). CONCLUSIONS:The lack of exome-wide significant genetic associations indicates an extreme heterogeneity of etiologies underlying SIDS. Our approach to understanding the genetic mechanisms of SIDS has far reaching implications for the SIDS research community as a whole and may catalyze new evidence-based SIDS research across multiple disciplines. Perturbations in glucocorticoid biosynthesis may represent a novel SIDS-associated biological pathway for future SIDS investigative research.
| S-EPMC6394853 | biostudies-literature
Project description:Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power.
| S-EPMC6697530 | biostudies-literature